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公开(公告)号：US20230085807A1
公开(公告)日：2023-03-23
申请号：US17664767
申请日：2022-05-24
Inventor： Weiqi DAI , Jinkai LIU , Hai JIN , Deqing ZOU
Abstract: The present invention provides an anti-trapdoor-leakage on-chain data restoration system, at least comprising: a blockchain node, for broadcasting transaction data of a request-initiating person to blockchain nodes and proposer nodes in other groups, respectively; and a proposer node, for performing computation of a Chameleon-Hash function using a key set that is generated by a key-generating module provided in the proposer node, packaging the transaction data to generate a new block, and distributing the new block to all the blockchain nodes so that the blockchain nodes update their respective underlying ledgers according to the new blocks broadcasted by the proposer. The system of the present invention not only realizes such functions as restoration and editing of the transaction data, but also protects operational security and reliability of blockchains.
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公开(公告)号：US20190281074A1
公开(公告)日：2019-09-12
申请号：US16109846
申请日：2018-08-23
Inventor： Hai JIN , Weiqi DAI , Yan XIA , Deqing ZOU
Abstract: The present invention involves with a cloud tenant oriented method and system for protecting privacy data. The method comprises at least the following steps: analyzing event handler information and/or behavioral signature information of request information and determining an execution mode, selecting at least one node without a behavioral signature plot to execute the tenant request and recording an execution result, generating a behavioral signature plot based on the execution result, and dynamically detecting security-sensitive behavior based on the behavioral signature plot. The present invention ensures data security during processing of security-sensitive data for cloud services by adopting a technology based on behavioral signatures, and prevents attackers from exploiting vulnerabilities and bypassing security control to conduct malicious operations. When there is no corresponding behavioral signature plots, multiples nodes are selected for processing of event handlers, and private data are dynamically protected based on behavioral signature plots, so as to assure secure execution results, provide fine-grained protection for security-sensitive behavior and protect data security while maintaining relatively low performance costs.
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3.发明公开公开(公告)号：US20240192929A1
公开(公告)日：2024-06-13
申请号：US18475447
申请日：2023-09-27
Inventor： Zhen LI , Ruqian ZHANG , Deqing ZOU , Hai JIN , Yangrui LI
Abstract: A sample-difference-based method and system for interpreting a deep-learning model for code classification is provided, wherein the method includes a step of off-line training an interpreter: constructing code transformation for every code sample in a training set to generate difference samples; generating difference samples respectively through feature deletion and code snippets extraction and then calculating feature importance scores accordingly; and inputting the original code samples, the difference samples and the feature importance scores into a neural network to get a trained interpreter; and a step of on-line interpreting the code samples: using the trained interpreter to extract important features from the snippets, then using an influence-function-based method to identify training samples that are most contributive to prediction, comparing the obtained important features and the most contributive training samples, and generating interpretation results for the object samples. The inventive system includes an off-line training module and an on-line interpretation module.
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公开(公告)号：US20250077683A1
公开(公告)日：2025-03-06
申请号：US18650308
申请日：2024-04-30
Inventor： Bin YUAN , Zijing XU , Tiancheng HU , Yueming WU , Deqing ZOU , Hai JIN
Abstract: The present disclosure relates to a system and method for vulnerability localization based on deep learning, which comprises, at a minimum, a processor configured to: analyze a code file under detection to obtain a first abstract syntax tree devoid of semantic information; build upon the first abstract syntax tree by incorporating data-flow edges and/or control-flow edges, thereby forming a second abstract syntax tree with semantic-flow enhancement; split the second abstract syntax tree to obtain a plurality of second abstract syntax sub-trees; and input these second abstract syntax sub-trees into a pre-established vulnerability detection and localization model. Compared with existing code vulnerability detection methods, the present disclosure employs a semantically-enhanced abstract syntax tree and finely-grained segmentation thereof, enabling both the efficient detection and accurate localization of code vulnerabilities, characterized by swift detection rates, low false positive rates, and commendable interpretability of the detection results.
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5.发明申请公开(公告)号：US20250013463A1
公开(公告)日：2025-01-09
申请号：US18650290
申请日：2024-04-30
Inventor： Zhen LI , Junyao YE , Deqing ZOU , Hai JIN , Xianghong ZENG
IPC: G06F8/73 , G06V10/764
Abstract: A method, system and processor for enhancing robustness of a source-code classification model based on invariant features is provided, wherein the method includes: combining non-robustness features to generate different style templates, converting codes in an input code training set into new codes of different styles to obtain a converted-code training set, merging the input-code and the converted-code training set into an expanded training set, and converting code texts in the expanded training set into code images; and converting the code images into required vectors, pairing samples of identical class randomly picked from the expanded training set and inputting the matched sample pairs into a feature extractor, iteratively updating the feature extractor and the matched sample pairs and extracting target characteristics, and training the extracted invariant features in a classifier to produce a trained model. The disclosed system includes a training set-expanding module and a model-training module.
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Patent Agency Ranking
公开(公告)号：US20230351035A1
公开(公告)日：2023-11-02
申请号：US17937995
申请日：2022-10-04
Inventor： Weiqi DAI , Liangliang YU , Hai JIN , Deqing ZOU
CPC classification number: G06F21/6218 , G06F21/602 , H04L9/0822 , H04L9/0825 , H04L9/0618 , H04L9/3221 , H04L9/3297
Abstract: The present invention relates to system and method for user-controllable sharing of authorization for private data, wherein the system at least comprises: a blockchain node, for recording and verifying transaction information and/or completing payment, a client, for encrypting a symmetric key into a re-encryption key to be sent to an IPFS node, so that after a re-encryption request it sends to the IPFS node is verified as valid, the client sends the symmetric key to a server; the IPFS node, for calling a zero-knowledge proof verification contract from the blockchain node in response to the re-encryption request from the client, and performing authorization and verification; a server, for sending first encrypted data involving user authorization to the IPFS node, and/or acquiring the symmetric key sent by the client and capable of decrypting authorization data. In the present invention, the control of authorized contents is transferred to the user from the service provider, enabling the user to control authorization. Besides, during authorization, authorization data contents, data flows and user behaviors are hidden, making use of the data protected from pry of service providers.
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公开(公告)号：US20240340298A1
公开(公告)日：2024-10-10
申请号：US18475471
申请日：2023-09-27
Inventor： Bin YUAN , Huan LIU , Jiajun REN , Qunjinming CHEN , Deqing ZOU , Hai JIN
IPC: H04L9/40
CPC classification number: H04L63/1425 , H04L63/166
Abstract: A method and system for recognizing TLS fingerprints based on finite-state machines is provided, wherein the system at least includes: a model inference module, for learning state machine models of target TLS implementations according to mapping information sent by a message mapping module; a fingerprint extracting module, for analyzing the state machine models and extracting multi-level fingerprints of the target TLS implementations; and a version recognizing module, for verifying the multi-level fingerprints for validity and/or recognizing version information of unknown TLS implementations. As compared to other network protocol identification systems, the present disclosure can identify and judge fine-grained information such as the specific implementation type and version of the specific TLS implementation. At the same time, the inventive method is highly automated, thereby ensuring good usability and scalability.
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8.发明公开公开(公告)号：US20240220647A1
公开(公告)日：2024-07-04
申请号：US18356004
申请日：2023-07-20
Inventor： Weiqi DAI , Ziyi LIANG , Kexuan ZHAO , Deqing ZOU , Hai JIN
CPC classification number: G06F21/6218 , H04N5/76
Abstract: The present invention relates to a blockchain-based system and method for preventing unauthorized deletion of surveillance video, the system at least comprising: a plurality of camera components, for generating surveillance video; at least one video-recording node, for connecting to a blockchain network and randomly generating a key pair, splitting the video file received from the camera components into a plurality of video-file blocks and randomly sending them to participation nodes across the blockchain network for storage, and recording hash values of the stored video files and node information; and a blockchain network device, for updating the hash values of the video files uploaded by the video-recording nodes based on a smart contract. In the present invention, uploading and storing time for camera-videos is recorded by the smart contract as tamper-resistant time reference for law enforcement, protecting video data from malicious deletion at video-recording nodes without the need of redundant space.
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公开(公告)号：US20230299938A9
公开(公告)日：2023-09-21
申请号：US17661988
申请日：2022-05-04
Inventor： Weiqi DAI , Shuyue TUO , Hai JIN , Deqing ZOU
CPC classification number: H04L9/0637 , H04L9/50 , H04L63/0442 , H04L63/105
Abstract: The present invention provides a system for privacy protection during IoT secure data sharing and a method thereof. The present invention relates to IoT data sharing, wherein it allows users to securely share data encrypted through decentralized attribute-based encryption on a blockchain-based platform without disclosing their attribute permission, so that individual users will not be identified according to their attributes, thereby protecting user privacy. The present invention also enables users sharing encrypted data and achieving traceability and accountability in the event of privacy breach. The present invention further provides an approach to verifying user permission using an attribute-based zero-knowledge proof, so as to securely and reliably verify whether permission of a data user is real. The present invention is suitable for solving existing problems about secure sharing and privacy protection of IoT data by verifying user identity and securely sharing user privacy data on a zero-knowledge basis.
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公开(公告)号：US20230087557A1
公开(公告)日：2023-03-23
申请号：US17661988
申请日：2022-05-04
Inventor： Weiqi DAI , Shuyue TUO , Hai JIN , Deqing ZOU
Abstract: The present invention provides a system for privacy protection during IoT secure data sharing and a method thereof. The present invention relates to IoT data sharing, wherein it allows users to securely share data encrypted through decentralized attribute-based encryption on a blockchain-based platform without disclosing their attribute permission, so that individual users will not be identified according to their attributes, thereby protecting user privacy. The present invention also enables users sharing encrypted data and achieving traceability and accountability in the event of privacy breach. The present invention further provides an approach to verifying user permission using an attribute-based zero-knowledge proof, so as to securely and reliably verify whether permission of a data user is real. The present invention is suitable for solving existing problems about secure sharing and privacy protection of IoT data by verifying user identity and securely sharing user privacy data on a zero-knowledge basis.
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